Diagnostic and prognostic systems, methods and computer program products are widely used to monitor, interpret and/or predict the health of a physical system. As is well known to those having skill in the art, diagnostics refers to determining the state of a part, component, subsystem or system with respect to its ability to perform its function according to design-intended parameters, whereas prognostics refers to predictive diagnostics which includes determining the remaining life, anticipated operational time-to-failure, and/or failure trajectory of a part, component, subsystem or system. In general, conventional diagnostic systems may use physical models of the physical system and/or predetermined nominal limits of sensor values to determine the health of the physical system. A typical scenario in the automotive industry may involve the on-board retention of diagnostic data, such as misfire flops in automotive engine control applications, followed by batch downloads of diagnostic information to base stations for further analysis and logistics decision support.
Conventional techniques that use predetermined normal limits of sensor values may use electronic lookup tables. Thus, current observations of sensor values may be compared against a lookup table of case-based histories of known behaviors. During field operations, baseline states of the system may be recorded as either normal or abnormal. Observed states are then compared with the historical states to determine whether they have been seen before and whether they are normal or not. While this approach can be performed quickly enough for relatively well behaved operations, it may not be suitable to complex behaviors, especially in changing environments when new behavior may be observed that is not in the library of observed states. Moreover, lookup table-based analysis may become exceedingly complex as the complexity of the physical system increases. The experimental baseline determination for vehicles, for example, may assume that all possible states encountered in the field can be predicted and captured in lookup tables for real-time diagnostics and prognostics. Moreover, pre-established lookup tables also may be based on vehicle platform averages, and may not provide granularity for individual vehicles. Finally, cross-sensor associations may be difficult to capture in conventional lookup tables.
Many other diagnostics and primitive prognostics techniques may be based on physical models that describe normal behavior under a range of different input parameters. For example, engine management might be based on physical modeling in the form of a set of equations, which may involve, for example, pressure, temperature and other variables. Such physical models, using a “reductionist” approach, may often be constrained to small parameter sets for mathematical tractability. As a result, comprehensive physical modeling for diagnostics of complex systems may be difficult, if not impossible. As an alternative to physical models, statistical approaches like neural computing may be used in some applications for pattern recognition. In these approaches, large data sets of attributes are obtained through observation of the system, usually during a test-and-validation stage. This data may then be used to fit statistical models that can be used to determine whether an observed state is “normal”. These techniques may require large data sets for model fitting, may not be adaptable over time and may be computationally prohibitive in a real-time environment.
Several potential difficulties may be associated with the use of the above approaches for complex systems and/or for real-time diagnostics. In particular, comprehensive physical modeling of complex systems for real-time applications may be difficult if not impossible, and the computational requirements may well be prohibitive. Moreover, the experimental baseline determination on sample physical systems may assume all possible states encountered in the field can be predicted and captured in lookup tables for real-time diagnostics and/or prognostics, and that the results are representative of an entire fleet of systems. Aging phenomena during real world operation also may be difficult to anticipate, since aging patterns are biased by history of individual use, and may not be easily extrapolated. Moreover, batch downloads from a complex system can add response delay and make real-time diagnostics difficult.
In summary, real-time diagnostics/prognostics during field operation may use continuous comparison and interpretation of the current system states with respect to design-intended performance baselines. Practical development of such onboard diagnostics may be hampered by the lack of analytical tools that are fast enough to keep up with the physical process in real-time, especially in the case of large, complex systems, where physical models may not be practically possible.